{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2add7af0-3fc7-47bf-8637-e45127561901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 1200\n",
      "   id                 internal_order_number  \\\n",
      "0   1  40611289-1885-43c9-a52d-a951cbee8828   \n",
      "1   2  0cd4a8b3-9d63-4cc3-8859-0b67e0265fdf   \n",
      "2   3  c127003a-5fd4-4b51-b2ba-168278da9e9d   \n",
      "3   4  d379c636-4eb1-44d9-97b3-fe42e11449f1   \n",
      "4   5  df1efbb7-3257-4ffa-aabf-f9db71859e2b   \n",
      "\n",
      "                    online_order_number store_name full_channel_user_id  \\\n",
      "0  8e57ae97-794f-4273-ad17-2870c51e202e       旗舰店A             VDXmZPXm   \n",
      "1  1004288f-944e-44fd-b94c-47ea0bdbb23b       旗舰店B             baIzQkAz   \n",
      "2  e1cfb656-fd70-4e96-80b3-ad8b34842341       旗舰店B             cYkodIXU   \n",
      "3  0b37f337-4232-4607-b007-228b2c49b780       旗舰店B             FcpblPNd   \n",
      "4  0513590e-c1fc-4a14-b962-bc3d71a628ba       线上店D             lBgdZxcC   \n",
      "\n",
      "        shipping_date        payment_date  payable_amount  paid_amount status  \\\n",
      "0 2022-05-07 20:03:32 2022-05-04 20:03:32          194.54       194.54    已发货   \n",
      "1 2022-05-04 16:03:43 2022-04-29 16:03:43          275.70       265.70    待发货   \n",
      "2 2022-08-31 07:50:37 2022-08-27 07:50:37          347.20       347.20    已完成   \n",
      "3 2022-03-09 17:16:47 2022-03-09 17:16:47          732.52       727.52    已完成   \n",
      "4 2022-07-17 13:13:13 2022-07-17 13:13:13          805.04       795.04    已发货   \n",
      "\n",
      "   ... unit_price product_name color_and_spec product_amount original_price  \\\n",
      "0  ...     184.54      环境加入其实.           白色 M         184.54         236.31   \n",
      "1  ...      53.14      不断积分留言.           黑色 M         265.70          65.10   \n",
      "2  ...     168.60        经验只有.           白色 M         337.20         201.81   \n",
      "3  ...     361.26      更多网站只是.           白色 M         722.52         404.52   \n",
      "4  ...     397.52        包括说明.           白色 M         795.04         475.34   \n",
      "\n",
      "  is_gift sub_order_status refund_status registered_quantity  \\\n",
      "0       否              已发货         未申请退款                   1   \n",
      "1       否              待发货         未申请退款                   5   \n",
      "2       否              已完成         未申请退款                   2   \n",
      "3       是              已完成         未申请退款                   2   \n",
      "4       否              已发货          成功退款                   2   \n",
      "\n",
      "  actual_refund_quantity  \n",
      "0                      0  \n",
      "1                      0  \n",
      "2                      0  \n",
      "3                      0  \n",
      "4                      2  \n",
      "\n",
      "[5 rows x 31 columns]\n",
      "\n",
      "仪表盘数据（前 12 行）：\n",
      "    刻度值  仪表盘\n",
      "0    50    0\n",
      "1    50   27\n",
      "2    60    0\n",
      "3    60   27\n",
      "4    70    0\n",
      "5    70   27\n",
      "6    80    0\n",
      "7    80   27\n",
      "8    90    0\n",
      "9    90   27\n",
      "10  100    0\n",
      "11  100   27\n",
      "\n",
      "指针相关参数： {'指针数值': 76, '指针角度': 70.2, '指针占位': 289.8}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Wedge, Circle\n",
    "from faker import Faker\n",
    "\n",
    "\n",
    "# ===================== 1. 生成模拟 erp_order 订单数据 =====================\n",
    "\n",
    "def generate_erp_orders(n_rows: int = 1200) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    基于给定的 erp_order 表结构，利用 Faker 生成模拟订单数据。\n",
    "    这里只生成与本次可视化相关的一部分字段，但字段名和类型与表结构保持一致。\n",
    "    \"\"\"\n",
    "    fake = Faker(\"zh_CN\")\n",
    "    records = []\n",
    "\n",
    "    # 一些枚举字段取值（可按需要再扩充）\n",
    "    store_names = [\"旗舰店A\", \"旗舰店B\", \"直营店C\", \"线上店D\"]\n",
    "    platforms = [\"天猫\", \"京东\", \"抖音\", \"快手\"]\n",
    "    provinces = [\"北京\", \"上海\", \"广东\", \"浙江\", \"江苏\", \"四川\"]\n",
    "    cities = {\n",
    "        \"北京\": [\"北京\"],\n",
    "        \"上海\": [\"上海\"],\n",
    "        \"广东\": [\"广州\", \"深圳\", \"佛山\"],\n",
    "        \"浙江\": [\"杭州\", \"宁波\"],\n",
    "        \"江苏\": [\"南京\", \"苏州\"],\n",
    "        \"四川\": [\"成都\", \"绵阳\"],\n",
    "    }\n",
    "    statuses = [\"已发货\", \"已完成\", \"待发货\"]\n",
    "    refund_status_list = [\"未申请退款\", \"退款中\", \"成功退款\", \"退款关闭\"]\n",
    "    is_gift_choices = [\"否\", \"是\"]\n",
    "\n",
    "    # 订单时间范围（避免 Faker 解析字符串出错，这里直接用 datetime）\n",
    "    start_dt = datetime(2022, 1, 1, 0, 0, 0)\n",
    "    end_dt = datetime(2022, 12, 31, 23, 59, 59)\n",
    "\n",
    "    for i in range(n_rows):\n",
    "        store = random.choice(store_names)\n",
    "        platform = random.choice(platforms)\n",
    "        province = random.choice(provinces)\n",
    "        city = random.choice(cities[province])\n",
    "\n",
    "        # 下单时间\n",
    "        order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "\n",
    "        # 付款时间 >= 下单时间\n",
    "        payment_date = order_time + timedelta(\n",
    "            minutes=random.randint(5, 240)\n",
    "        )\n",
    "\n",
    "        # 发货时间 >= 付款时间，保留少量“未发货”记录\n",
    "        if random.random() < 0.9:\n",
    "            shipping_date = payment_date + timedelta(days=random.randint(0, 5))\n",
    "        else:\n",
    "            shipping_date = None\n",
    "\n",
    "        # 商品与金额信息\n",
    "        spu = f\"SPU{random.randint(1000, 9999)}\"\n",
    "        sku = f\"{spu}-{random.choice(['S', 'M', 'L', 'XL'])}\"\n",
    "        quantity = random.randint(1, 5)\n",
    "        unit_price = round(random.uniform(50, 500), 2)  # 单价>1，满足视图过滤条件\n",
    "        product_amount = round(unit_price * quantity, 2)\n",
    "        original_price = round(unit_price * random.uniform(1.0, 1.5), 2)\n",
    "\n",
    "        # 订单维度金额：商品金额 + 固定运费 10 元，有少量折扣\n",
    "        payable_amount = product_amount + 10\n",
    "        discount = random.choice([0, 5, 10])\n",
    "        paid_amount = max(0.0, payable_amount - discount)\n",
    "\n",
    "        status = random.choice(statuses)\n",
    "        refund_status = random.choices(\n",
    "            refund_status_list, weights=[0.75, 0.05, 0.1, 0.1]\n",
    "        )[0]\n",
    "        is_gift = random.choices(is_gift_choices, weights=[0.9, 0.1])[0]\n",
    "\n",
    "        record = {\n",
    "            \"id\": i + 1,\n",
    "            \"internal_order_number\": fake.uuid4(),\n",
    "            \"online_order_number\": fake.uuid4(),\n",
    "            \"store_name\": store,\n",
    "            \"full_channel_user_id\": fake.pystr(min_chars=8, max_chars=8),\n",
    "            \"shipping_date\": shipping_date,\n",
    "            \"payment_date\": payment_date,\n",
    "            \"payable_amount\": round(payable_amount, 2),\n",
    "            \"paid_amount\": round(paid_amount, 2),\n",
    "            \"status\": status,\n",
    "            \"consignee\": fake.name(),\n",
    "            \"spu\": spu,\n",
    "            \"order_time\": order_time,\n",
    "            \"province\": province,\n",
    "            \"city\": city,\n",
    "            \"platform\": platform,\n",
    "            \"sub_order_number\": fake.uuid4(),\n",
    "            \"online_sub_order_number\": fake.uuid4(),\n",
    "            \"original_online_order_number\": fake.uuid4(),\n",
    "            \"sku\": sku,\n",
    "            \"quantity\": quantity,\n",
    "            \"unit_price\": unit_price,\n",
    "            \"product_name\": fake.sentence(nb_words=3),\n",
    "            \"color_and_spec\": random.choice([\"黑色 S\", \"黑色 M\", \"白色 M\", \"蓝色 L\"]),\n",
    "            \"product_amount\": product_amount,\n",
    "            \"original_price\": original_price,\n",
    "            \"is_gift\": is_gift,\n",
    "            \"sub_order_status\": status,\n",
    "            \"refund_status\": refund_status,\n",
    "            \"registered_quantity\": quantity,\n",
    "            \"actual_refund_quantity\": (\n",
    "                0 if refund_status != \"成功退款\" else random.randint(1, quantity)\n",
    "            ),\n",
    "        }\n",
    "        records.append(record)\n",
    "\n",
    "    df = pd.DataFrame(records)\n",
    "    return df\n",
    "\n",
    "\n",
    "# ===================== 2. 构造与 Excel 一致的仪表盘数据 =====================\n",
    "\n",
    "def build_gauge_data():\n",
    "    \"\"\"\n",
    "    构造与截图中相同的刻度与仪表盘数据，\n",
    "    以及指针数值=76、指针角度=70.2、指针占位=289.8。\n",
    "    \"\"\"\n",
    "    # 刻度值 50~150，步长 10\n",
    "    ticks = list(range(50, 151, 10))\n",
    "\n",
    "    # 每个刻度两行：仪表盘列依次为 0、27，最后一个刻度为 0、90\n",
    "    gauge_ticks = []\n",
    "    gauge_values = []\n",
    "    for t in ticks:\n",
    "        gauge_ticks.extend([t, t])\n",
    "        if t < 150:\n",
    "            gauge_values.extend([0, 27])\n",
    "        else:\n",
    "            gauge_values.extend([0, 90])\n",
    "\n",
    "    gauge_df = pd.DataFrame({\n",
    "        \"刻度值\": gauge_ticks,\n",
    "        \"仪表盘\": gauge_values\n",
    "    })\n",
    "\n",
    "    # 指针数值固定为 76（与 Excel 相同）\n",
    "    needle_value = 76\n",
    "    min_v, max_v = 50, 150\n",
    "    total_angle = 270  # 10 段 * 27 度\n",
    "\n",
    "    # 指针相对于最小刻度的角度：70.2°\n",
    "    needle_delta = (needle_value - min_v) / (max_v - min_v) * total_angle\n",
    "    # 在整圆中的实际占位角度：360 - 70.2 = 289.8°\n",
    "    needle_absolute = 360 - needle_delta\n",
    "\n",
    "    pointer_meta = {\n",
    "        \"指针数值\": needle_value,\n",
    "        \"指针角度\": round(needle_delta, 1),   # 70.2\n",
    "        \"指针占位\": round(needle_absolute, 1), # 289.8\n",
    "    }\n",
    "\n",
    "    return gauge_df, pointer_meta\n",
    "\n",
    "\n",
    "# ===================== 3. 画图：复刻 Excel 风格的多层圆环仪表盘 =====================\n",
    "\n",
    "def plot_gauge(gauge_df: pd.DataFrame, pointer_meta: dict):\n",
    "    # 全局中文字体（Windows 下一般都有 SimHei / 微软雅黑）\n",
    "    plt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"Microsoft YaHei\"]\n",
    "    plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 7.5))\n",
    "    # 深色背景\n",
    "    fig.patch.set_facecolor(\"#171C3A\")\n",
    "\n",
    "    # 主标题\n",
    "    fig.suptitle(\n",
    "        \"2022年6月29公司整体运营指数良好\",\n",
    "        fontsize=26,\n",
    "        color=\"white\",\n",
    "        fontweight=\"bold\",\n",
    "        y=0.92,\n",
    "    )\n",
    "\n",
    "    # 白色图表区域（中间的那块白色矩形）\n",
    "    ax = fig.add_axes([0.18, 0.08, 0.64, 0.72])\n",
    "    ax.set_facecolor(\"white\")\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "    # 圆心与各层半径\n",
    "    center = (0, 0)\n",
    "    outer_r = 1.0\n",
    "    middle_r = 0.82\n",
    "    inner_r = 0.64\n",
    "    hole_r = 0.35\n",
    "\n",
    "    # ---------- 外层蓝色整圈 ----------\n",
    "    outer_ring = Wedge(\n",
    "        center, outer_r, 0, 360,\n",
    "        width=outer_r - middle_r,\n",
    "        facecolor=\"#0067C2\",\n",
    "        edgecolor=\"none\"\n",
    "    )\n",
    "    ax.add_patch(outer_ring)\n",
    "\n",
    "    # ---------- 中层彩色区段 ----------\n",
    "    ticks_unique = sorted(gauge_df[\"刻度值\"].unique())\n",
    "    min_tick, max_tick = ticks_unique[0], ticks_unique[-1]\n",
    "    total_angle = 270.0\n",
    "    # 50 对应 225°，150 对应 -45°（315°），形成一个 270° 的仪表半环\n",
    "    start_angle = 225.0\n",
    "\n",
    "    def value_to_angle(v):\n",
    "        \"\"\"将刻度值映射到角度（数学坐标，逆时针为正）。\"\"\"\n",
    "        frac = (v - min_tick) / (max_tick - min_tick)\n",
    "        return start_angle - frac * total_angle\n",
    "\n",
    "    def band_color(v_mid):\n",
    "        \"\"\"根据区间中点决定颜色，和 Excel 配色大致对应。\"\"\"\n",
    "        if 50 <= v_mid < 80:\n",
    "            return \"#0F6E6E\"   # 蓝绿色\n",
    "        elif 80 <= v_mid < 110:\n",
    "            return \"#F4B424\"   # 黄色\n",
    "        elif 110 <= v_mid <= 150:\n",
    "            return \"#F5456C\"   # 红色\n",
    "        else:\n",
    "            return \"#0067C2\"   # 默认蓝色\n",
    "\n",
    "    # 50-60, 60-70, ..., 140-150 每一段\n",
    "    for v1, v2 in zip(ticks_unique[:-1], ticks_unique[1:]):\n",
    "        angle1 = value_to_angle(v1)\n",
    "        angle2 = value_to_angle(v2)\n",
    "        v_mid = (v1 + v2) / 2\n",
    "        color = band_color(v_mid)\n",
    "\n",
    "        wedge = Wedge(\n",
    "            center,\n",
    "            middle_r,\n",
    "            angle2,  # 注意：逆时针方向，角度顺序要反过来\n",
    "            angle1,\n",
    "            width=middle_r - inner_r,\n",
    "            facecolor=color,\n",
    "            edgecolor=\"white\",\n",
    "            linewidth=2,\n",
    "        )\n",
    "        ax.add_patch(wedge)\n",
    "\n",
    "    # 在 150 后面再延伸一段蓝色到下方，模拟 Excel 中最后 90 度的蓝色过渡\n",
    "    angle_150 = value_to_angle(150)\n",
    "    extra_end = angle_150 - 63  # 近似 90°-一小段\n",
    "    extra_wedge = Wedge(\n",
    "        center,\n",
    "        middle_r,\n",
    "        extra_end,\n",
    "        angle_150,\n",
    "        width=middle_r - inner_r,\n",
    "        facecolor=\"#0067C2\",\n",
    "        edgecolor=\"white\",\n",
    "        linewidth=2,\n",
    "    )\n",
    "    ax.add_patch(extra_wedge)\n",
    "\n",
    "    # ---------- 内层深色圆环 ----------\n",
    "    inner_ring = Wedge(\n",
    "        center, inner_r, 0, 360,\n",
    "        width=inner_r - hole_r,\n",
    "        facecolor=\"#151C3A\",\n",
    "        edgecolor=\"none\"\n",
    "    )\n",
    "    ax.add_patch(inner_ring)\n",
    "\n",
    "    # 中间白色空心\n",
    "    center_circle = Circle(center, hole_r, facecolor=\"white\", edgecolor=\"none\")\n",
    "    ax.add_patch(center_circle)\n",
    "\n",
    "    # ---------- 刻度数字 ----------\n",
    "    for tick in ticks_unique:\n",
    "        angle = np.deg2rad(value_to_angle(tick))\n",
    "        r = (inner_r + hole_r) / 2 + 0.02  # 数字半径\n",
    "        x = r * np.cos(angle)\n",
    "        y = r * np.sin(angle)\n",
    "        ax.text(\n",
    "            x,\n",
    "            y,\n",
    "            str(tick),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            color=\"white\",\n",
    "            fontsize=14,\n",
    "        )\n",
    "\n",
    "    # ---------- 指针 ----------\n",
    "    needle_value = pointer_meta[\"指针数值\"]\n",
    "    needle_angle = np.deg2rad(value_to_angle(needle_value))\n",
    "    needle_r = inner_r\n",
    "    x_end = needle_r * np.cos(needle_angle)\n",
    "    y_end = needle_r * np.sin(needle_angle)\n",
    "\n",
    "    ax.plot(\n",
    "        [0, x_end],\n",
    "        [0, y_end],\n",
    "        linewidth=3,\n",
    "        color=\"#F4F4F4\",\n",
    "        alpha=0.9,\n",
    "    )\n",
    "\n",
    "    # 指针中心小圆点\n",
    "    ax.add_patch(Circle(center, 0.015, facecolor=\"#CCCCCC\", edgecolor=\"none\"))\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ===================== 主流程：先造数，再画图 =====================\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # 1. 生成模拟订单数据（≥1000 条）\n",
    "    df_erp = generate_erp_orders(1200)\n",
    "    print(\"模拟订单数据条数：\", len(df_erp))\n",
    "    print(df_erp.head())\n",
    "\n",
    "    # 2. 构造与 Excel 一致的仪表盘数据\n",
    "    gauge_df, pointer_meta = build_gauge_data()\n",
    "    print(\"\\n仪表盘数据（前 12 行）：\")\n",
    "    print(gauge_df.head(12))\n",
    "    print(\"\\n指针相关参数：\", pointer_meta)\n",
    "\n",
    "    # 3. 画 Excel 风格的仪表盘\n",
    "    plot_gauge(gauge_df, pointer_meta)\n"
   ]
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